The weighted tunable clustering in local-world networks with incremental behaviors

TL;DR

This paper introduces a weighted local-world network model incorporating increment behavior and tunable clustering, analyzing its properties and impact on epidemic spreading, revealing significant effects of clustering on dynamics.

Contribution

It proposes a novel weighted local-world network model with tunable clustering and investigates its properties and epidemic spreading behavior.

Findings

Model exhibits scale-free degree distribution

Tunable clustering affects epidemic dynamics

Vertices show assortative mixing

Abstract

Since some realistic networks are influenced not only by increment behavior but also by tunable clustering mechanism with new nodes to be added to networks, it is interesting to characterize the model for those actual networks. In this paper, a weighted local-world model, which incorporates increment behavior and tunable clustering mechanism, is proposed and its properties are investigated, such as degree distribution and clustering coefficient. Numerical simulations are fit to the model characters and also display good right skewed scale-free properties. Furthermore, the correlation of vertices in our model is studied which shows the assortative property. Epidemic spreading process by weighted transmission rate on the model shows that the tunable clustering behavior has a great impact on the epidemic dynamic. Keywords: Weighted network, increment behavior, tun- able cluster, epidemic spreading.